Invasive Phenoprofiling of Acute-Myocardial-Infarction-Related Cardiogenic Shock

Background: Studies had previously identified three cardiogenic shock (CS) phenotypes (cardiac-only, cardiorenal, and cardiometabolic). Therefore, we aimed to understand better the hemodynamic profiles of these phenotypes in acute myocardial infarction-CS (AMI-CS) using pulmonary artery catheter (PAC) data to better understand the AMI-CS heterogeneity. Methods: We analyzed the PAC data of 309 patients with AMI-CS. The patients were classified by SCAI shock stage, congestion profile, and phenotype. In addition, 24 h hemodynamic PAC data were obtained. Results: We identified three AMI-CS phenotypes: cardiac-only (43.7%), cardiorenal (32.0%), and cardiometabolic (24.3%). The cardiometabolic phenotype had the highest mortality rate (70.7%), followed by the cardiorenal (52.5%) and cardiac-only (33.3%) phenotypes, with significant differences (p < 0.001). Right atrial pressure (p = 0.001) and pulmonary capillary wedge pressure (p = 0.01) were higher in the cardiometabolic and cardiorenal phenotypes. Cardiac output, index, power, power index, and cardiac power index normalized by right atrial pressure and left-ventricular stroke work index were lower in the cardiorenal and cardiometabolic than in the cardiac-only phenotypes. We found a hazard ratio (HR) of 2.1 for the cardiorenal and 3.3 for cardiometabolic versus the cardiac-only phenotypes (p < 0.001). Also, multi-organ failure, acute kidney injury, and ventricular tachycardia/fibrillation had a significant HR. Multivariate analysis revealed that CS phenotypes retained significance (p < 0.001) when adjusted for the Society for Cardiovascular Angiography & Interventions score (p = 0.011) and ∆congestion (p = 0.028). These scores independently predicted mortality. Conclusions: Accurate patient prognosis and treatment strategies are crucial, and phenotyping in AMI-CS can aid in this effort. PAC profiling can provide valuable prognostic information and help design new trials involving AMI-CS.


Introduction
Acute myocardial infarction complicated by cardiogenic shock (AMI-CS) has a looming prognosis.Recently, the Society for Cardiovascular Angiography & Interventions (SCAI)

Results
The phenotypes were cardiac (43.7%), cardiorenal (32%), and cardiometabolic (24.3%).The cardiac group was the youngest compared with the cardiorenal and cardiometabolic groups (p < 0.001).The prevalence of hypertension was higher in the cardiorenal and cardiometabolic compared with the cardiac group (59.6% and 58.7% vs. 42.2%;p = 0.026 and 0.012, respectively).The incidence of previous chronic kidney disease was also higher in the cardiorenal and cardiometabolic compared with the cardiac group.The proportion of patients with diabetes mellitus was also higher but not significantly different in the cardiorenal group (p = 0.115).
Previous MI, percutaneous coronary intervention (PCI), and coronary artery bypass graft (CABG) were not significantly different among the three groups.There was a significant difference in the type of AMI (p = 0.022), with a higher percentage of STEMI in the cardiometabolic group (93.3%) compared with the other groups.The out-hospital cardiac arrest was not different (p = 0.92).The Killip-Kimball classification showed significant differences (p < 0.001), with a higher proportion of class IV in the cardiometabolic group.
The results show that the cardiometabolic group had the lowest left-ventricular ejection fraction (LVEF) of 30% compared with 35% in the cardiac and 32% in the cardiorenal group; however, this was not statistically significant (p = 0.073).
White blood cell counts were higher, and platelets were lower, in the cardiometabolic group compared to the cardiorenal and cardiac groups.Glucose levels were significantly higher in the cardiometabolic compared with the cardiac (p = 0.02) and the cardiorenal groups (p = 0.034).BUN and creatinine levels were significantly higher in the cardiorenal group (p < 0.001).The eGFR was considerably lower in the cardiorenal and cardiometabolic groups (p < 0.001).
Values of aspartate transaminase (AST), ALT, lactate dehydrogenase (LDH), maximum creatinine, maximum AST, maximum ALT, bilirubin, and lactate significantly differed in the groups, with the highest values found in the cardiometabolic group.The cardiometabolic group had lower base excess and pH values (p < 0.001).
Norepinephrine was used in 74.1% of the cardiac, 82.8% of the cardiorenal, and 88% of the cardiometabolic groups (p = 0.039).Vasopressin was used in 45.9% of the cardiac, 56.6% of the cardiorenal, and 73.3% of the cardiometabolic groups (p = 0.001).Levosimendan was used in 25.9%, 27.3%, and 48% of the cardiac, cardiorenal, and cardiometabolic groups, respectively (p = 0.002).Dobutamine use was lower in 76.3% of the cardiac group compared with 83.8% and 97.3% of the cardiorenal and cardiometabolic groups, respectively.Considering the number of vasoactive drugs, a higher proportion of the cardiometabolic group had four drugs compared with the cardiac and cardiorenal groups (p < 0.001).
No differences were seen regarding mechanical circulatory support (MCS), with the majority being intra-aortic balloon pumps, with only two patients with a cardiorenal phenotype receiving extracorporeal membrane oxygenation and only two patients receiving Impella-CP (one each in the cardiorenal and cardiometabolic groups, respectively).
The incidence of AKI was higher in the cardiorenal (82.8%) and cardiometabolic (92%) groups compared with the cardiac group (43%) (p < 0.001).Furthermore, the severity of AKI was higher in the cardiometabolic and cardiorenal compared with the cardiac group (p < 0.001).
Significant differences existed in the number of organ failures and the presence of MOF.A higher percentage of patients with cardiometabolic comorbidities had MOF than in the other groups (80% vs. 62.6% in the cardiorenal and 41.5% in the cardiac group; p < 0.001).In addition, a higher percentage of patients with the cardiometabolic phenotype had ≥4 more organ failures than in the other groups (33.3% vs. 18.2% in the cardiorenal and 6.7% in the cardiac groups; p < 0.001).
The cardiometabolic group had higher MODS scores, indicating higher organ dysfunction levels than in the other groups (p < 0.001).The same pattern was seen with the SCAI score, with patients in the cardiometabolic and cardiorenal groups having higher scores (p < 0.001).
Finally, the highest mortality rate was seen in the cardiometabolic (70.7%), followed by the cardiorenal (52.5%) and cardiac groups (33.3%), with significant differences (p < 0.001; see Table 1 and full pairwise comparison in Supplementary Table S1).

Hemodynamic Variables and CS phenotypes
Heart rate did not show any significant differences in the three groups as a whole and at any time point (F = 0.44; p = 0.644), nor was any interaction seen in the within-subjects' effects.
However, SBP showed significant differences between subjects, where the effects were lower in the cardiometabolic and higher in the cardiac groups at all time points (F = 3.78; p = 0.024).In the point comparison, only at 24 h do we see a difference in the multiple comparison adjustment (p = 0.008), with lower SBP in the cardiometabolic compared with the cardiac group (p = 0.007) but not the cardiorenal group (p = 0.067).Significant differences in mean arterial pressure (MAP), similar to SBP, were seen between groups (F = 3.52; p = 0.031), but only at 24 h did we see differences in the time-point analysis.Considering DBP, no differences were observed in the between-or within-subjects effects, but only at 24 h did the cardiac group show a higher DBP (p = 0.007).In addition, a difference in comparison with the cardiometabolic group was seen in the multiple comparisons (p = 0.01).
Perfusion pressure (MAP-RAP) showed significant group differences and higher F values than its derived components (F = 8.17; p < 0.001), with lower values in the cardiorenal and cardiometabolic groups in the multiple comparisons.However, all time points showed a difference after 6 h.In the pairwise comparison, we saw differences between the cardiac vs. the cardiometabolic group and at 24 h vs. the cardiorenal group.
RAP showed significant differences among the groups (F = 6.64; p = 0.001), with higher values in the cardiometabolic and cardiorenal groups for all time points.Significant differences were seen at all time points in the point data analysis.When corrected by multiple comparisons at 12 and 24 h, differences were seen between the cardiac and cardiorenal groups and at all time points for the cardiometabolic group.PCWP showed significant differences between groups (F = 4.71; p = 0.01), with the lowest values in the cardiac group.This was only significant in the time-point analysis at the 24 h mark.
Considering pulmonary artery systolic pressure (PASP), no difference was initially seen in the analyses of variance (ANOVAs) between subjects, but the values decreased over time, and lower values were seen in the cardiac group at the 24 h time-point analysis.However, the significance was not retained in the pairwise analysis.PA diastolic pressure (PADP) had differences in the group • ime interaction, with lower values in the cardiac group and a tendency to decrease compared with the increasing levels in the cardiorenal and cardiometabolic profiles.The point analysis showed differences only at 24 h, with the lowest values in the cardiac group, which, in the pairwise comparisons, only significantly differed from the cardiometabolic group.Considering the mean PA pressure (mPAP), no differences between subjects were seen in the ANOVA results.In the time-point analysis, differences were seen at 6 h, with the highest values in the cardiometabolic profile.At 24 h, the cardiac group had the lowest values, although no differences were seen in the pairwise analysis.Considering the pulmonary artery pressure index (PAPi), no differences were seen between the groups in ANOVA or time-point analyses.
Cardiac output, index, power, power index, and CPI (RAP) showed similar behavior when using ANOVA, and differences arose in the between-group comparison (p < 0.05), with the lowest cardiac output and derived indexes in the cardiometabolic group and the highest levels in the cardiac group.In the time-point analysis, all times showed differences, with a pairwise comparison showing lower levels in cardiometabolic patients vs. cardiac patients; only at 6 h did the cardiorenal group show higher levels of cardiac output, index, power, and CPI (RAP) than the cardiometabolic group.(Table 2, Figures 1 and 2, and Supplementary Tables S2 and S3) the pairwise comparison with the cardiac and cardiorenal groups.However, differences were only seen in contrast with the cardiac group at 6 and 12 h.Systemic vascular resistance (SVR) but not the SVR index (SVRi) had significant group differences in the ANOVA results (F = 3.1, p = 0.046).These differences were further enhanced in the time-point analysis, and only at 6 h did SVR and SVRi show some higher differences in the cardiometabolic group.However, neither pulmonary vascular resistance (PVR) nor PVR index (PVRi) did not achieve statistical differences among the groups.In the time-point analysis, only at 6 and 12 h did PVR have significant differences, with the lowest values in the cardiac group, and PVRi only achieved this at 6 h.
The left-ventricular stroke work index (LVSWi) showed differences (F = 7.18, p = 0.001), with the lowest values seen in the cardiometabolic group along the four-time points compared with the cardiac group.However, the right-ventricular stroke work index (RVSWi) did not show these differences.Only at the baseline were significant differences between the cardiometabolic and cardiac groups.Stroke volume (SV) and stroke volume index (SVi) data show differences in both SV and SVI (F = 6.91, p = 0.001 and F = 4.63, p = 0.01, respectively).In the time-point analysis, the cardiometabolic group had far worse hemodynamic parameters at baseline in the pairwise comparison with the cardiac and cardiorenal groups.However, differences were only seen in contrast with the cardiac group at 6 and 12 h.Systemic vascular resistance (SVR) but not the SVR index (SVRi) had significant group differences in the ANOVA results (F = 3.1, p = 0.046).These differences were further enhanced in the time-point analysis, and only at 6 h did SVR and SVRi show some higher differences in the cardiometabolic group.However, neither pulmonary vascular resistance (PVR) nor PVR index (PVRi) did not achieve statistical differences among the groups.In the time-point analysis, only at 6 and 12 h did PVR have significant differences, with the lowest values in the cardiac group, and PVRi only achieved this at 6 h.
The left-ventricular stroke work index (LVSWi) showed differences (F = 7.18, p = 0.001), with the lowest values seen in the cardiometabolic group along the four-time points compared with the cardiac group.However, the right-ventricular stroke work index (RVSWi) did not show these differences.Only at the baseline were significant differences between the cardiometabolic and cardiac groups.

Hemodynamic Variables and Multi-Organ Failure, AKI, and Ventricular Arrhythmias
Multi-organ failure: Regarding CPI(RAP), lower values with statistical significance were seen in the MOF group.In the time-point analysis at baseline, 6, 12, and 24 h, lower perfusion pressure was seen in MOF.LVSWi had differences more marked in this group, with all these hemodynamic parameters significantly different at all time points (Figure S1A-C).

Hemodynamic Variables and Multi-Organ Failure, AKI, and Ventricular Arrhythmias
Multi-organ failure: Regarding CPI (RAP) , lower values with statistical significance were seen in the MOF group.In the time-point analysis at baseline, 6, 12, and 24 h, lower perfusion pressure was seen in MOF.LVSWi had differences more marked in this group, with all these hemodynamic parameters significantly different at all time points (Figure S1A-C).
AKI: CPI (RAP) had significant differences overall lower in patients with AKI at all time points.SBP, MAP, and perfusion pressure were significant in the ANOVA, with lower values in patients with AKI.PCWP had substantial differences (F = 4.42, p = 0.036) with higher values in AKI, which was seen at 6 and 24 h in the time-point analysis.LVSWi had differences with lower values in AKI, but in the time-point analysis, this was seen from 6 to 24 h (Figure S1D-F).
Ventricular tachycardia or fibrillation: Low CPI (RAP) , perfusion pressure, and LVSWi were seen in the ventricular tachycardia/fibrillation (VT/VF) in the first 24 h of active hemodynamic monitoring (Figure S1G-I).(Table 3, for Detailed Description, See the Supplementary Text, Table S3, Figure S1).

Multivariate Analysis
When adjusted for SCAI and ∆congestion, the phenotyping retained its significance (p < 0.001); furthermore, SCAI and ∆congestion also appeared to have an independent value for mortality prediction (p = 0.011 and 0.028, respectively), with an AUC of 0.72 (0.67-0.77; Figure 3E).

Discussion
Herein, we describe the full invasive hemodynamic profiling of AMI-CS.To our knowledge, this is the first attempt to define a three-axis model of CS profiling (phenotype + SCAI + congestion).Previously, the proposed phenotypes by Zweck et al. [2] correctly classified specific higher mortality groups; we aimed to allocate the patient groups into specific phenotypes by utilizing alanine aminotransferase (ALT) and estimated glomerular filtration rate (eGFR) as straightforward indicators.Moreover, the independent mortality estimated by SCAI or change in congestion could help to allocate high-intensity therapies, such as MCS, or other resourceful interventions.We also demonstrated that complications, such as MOF, AKI, and VT/VF, increased mortality in AMI-CS.
CS phenotypes exhibit a distinct hemodynamic signature, with the cardiometabolic group demonstrating the worst hemodynamic parameters.Regarding congestion, previous studies have shown that patients in the cardiorenal and cardiometabolic groups have higher RAP and PCWP levels, indicating that they have trouble achieving decongestion compared with the cardiac group [2,3].As expected, patients in the cardiac group had the highest cardiac output and power and their derived measures in our study, while patients in the cardiometabolic group had the lowest levels.
The best hemodynamic parameters to distinguish between the groups were cardiac power followed by CPI (RAP) .Baldetti et al. suggests that a cut-off of 0.28 W/m 2 indicates an increased risk of mortality in a time-fixed manner [11].We saw that the cardiometabolic group had more trouble achieving higher values than the other two groups.These findings, derived from perfusion pressure, suggest that increased congestion is observed in the cardiorenal and cardiometabolic groups, and they can identify more splanchnic damage (renal and liver) [11].PADP showed a particular response based on the group type.Patients in the cardiac group had lower levels of PADP, while patients in the cardiorenal and cardiometabolic groups had increased levels as the first 24 h progressed.LVSWi also showed good discriminative power; previous studies suggested that it can better discriminate mortality risk than LVEF and improve mortality risk stratification [12].This could aid in characterizing phenotypes, as a step up in LVSWi is observed in cardiorenal and cardiac groups in contrast to the cardiometabolic group.
Interestingly, the vasoactive analysis reveals intriguing association differences in usage among the different CS phenotypes.Vasopressin and dobutamine showed more pronounced disparities, with cardiometabolic patients often requiring higher percentages.Also, levosimendan showed a higher use in cardiorenal and cardiometabolic groups.These associations suggest that CS phenotypes may have varying hemodynamic needs, possibly linked to their underlying phenotype-related pathophysiology and the severity of organ involvement, which is usually more pronounced in the cardiometabolic group.Further research is needed to uncover the mechanisms behind these differences and their implications in CS management, as these associations probably underlie the higher MOF seen in the cardiorenal and cardiometabolic phenotypes.
MOF development could be discerned from the first 24 h hemodynamics.As suggested by previous studies, CPI (RAP) had the best discriminative power, followed by perfusion pressure.An inadequate pressure-flow state, which is globally measured by CPI (RAP) and impaired in MOF, compromises tissular metabolic demands, which leads to end-organ failure.In the CardShock study [13], variables such as confusion, elevated blood lactate, and eGFR were predictors of in-hospital mortality, as in the MODS system [10].Thus, it is important to underscore the usefulness of CPI (RAP) and also LVSWi as a hemodynamic goal and a discriminative power to identify patients who develop MOF [11,13,14].Lower PAPi levels in MOF suggest that these patients had an overall worse RV function, which is also supported by the fact that these patients had more RV congestion, as seen by higher RAP levels [15].Therefore, as proposed previously [4], an effective rapid decongestion is paramount to avoiding MOF.
The development of AKI has been associated with higher overall mortality.Unlike previous studies on the cardiorenal syndrome that have mainly focused on heart failure and that failed to find an association between cardiac index and AKI development [16,17], our study revealed that lower levels of cardiac power and output, as well as their derived measurements, had an impact on the development of AKI.In addition, CPI (RAP) was found to have the best discriminative power, possibly because of the different hemodynamic responses in the acute setting of AMI-CS [11,14].Finally, there were substantial differences in PCWP and RAP, with the latter observed not between groups but as an interaction with time.High LV congestion and ineffective RV/LV decongestion led to AKI development.
Few studies have investigated the relationship between hemodynamics and arrhythmogenesis in cases of electrical instability.Typically, the underlying mechanism is an ischemia-induced insult resulting in low pressures.The most useful parameter for pre-dicting arrhythmogenesis, as with previous complications, is CPI (RAP) .While VT/FV has been extensively studied in advanced HF [18], there is a lack of research in patients with AMI-CS, highlighting the importance of predicting this complication.Our cohort shows low pressure-flow parameters among patients who develop these arrhythmias.Thus, achieving an adequate pressure-flow state is crucial, and the optimization by MCS or pharmacological treatment could potentially prevent arrhythmia development.
The cardiogenic shock profiling aids to provide a more granular classification of the classic types of shock (cardiac, hypovolemic, septic, etc.).In our study's three-axis model for subsets of AMI-CS, profiling offers clinicians a tool to personalize treatments, optimizing resource allocation and ultimately improving patient outcomes.Also, axis phenotyping could help us design appropriate granular data to study patients that might benefit from MCS [3], especially VA-ECMO, since trials showed no reduction in 30-day outcomes in AMI-CS all-comers [19,20].
The current study's limitations are its single-center retrospective data, the lack of a specific time for complications, and the lack of records of the particular vasoactive drug dose, the timing of MCS, and the response to PAC-derived hemodynamic data, which prevents the calculation of other scores, such as SOFA.In addition, as the cohort has inherent mortality or PAC withdrawal losses, the expectation-maximization algorithm's intrinsic limitations impact the current data.Nevertheless, this method helps us to understand the hemodynamic trajectories and is more informative and statistically rigorous [21,22].The strengths of the present study are the large cohort and the full record of the hemodynamic profiling in an academic center, which kept all specific primary PAC-derived data, which contrasts with the scarce complete PAC profiling for AMI-CS in a previous registry [5].The longitudinal PAC measures described here, and the dynamic nature of AMI-CS could help improve our understanding of this high mortality entity, develop prevention strategies, and allocate resources more effectively.

Conclusions
Comprehensive phenotyping in AMI-CS can provide valuable patient-level prognostic information.The phenotyping of cardiogenic shock reveals varying mortality rates and complications.In addition, specific hemodynamic behaviors can signal potentially high-risk complications, such as MOF, AKI, and/or ventricular arrhythmias.Therefore, complete phenotyping in patients with AMI-CS is crucial for providing accurate prognosis and for the design of new trials.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/jcm12185818/s1, Figure S1: ANOVA repeated measures of representative hemodynamic parameters in CS-AMI complications.In multi-organ failure, cardiac power index(RAP) (A), perfusion pressure (B), and left ventricular stroke work index (C).In acute kidney injury, cardiac power index(RAP) (D), left ventricular stroke work index (E), and perfusion pressure (F).For VT/VF (ventricular tachycardia/fibrillation), cardiac power index (RAP) (G), left ventricular stroke work index (C), and mean arterial pressure (I).; Table S1.Pairwise comparison with Bonferroni correction of the characteristics of patients with acute myocardial infarction cardiogenic shock phenotype.Table S2.Pairwise comparison of hemodynamics parameters in AMI-CS phenotypes.Table S3.ANOVA analysis of the phenotypes and complications in acute myocardial infarction complicated by cardiogenic shock.Table S4.Coronary Artery Distribution in Different Cardiogenic Shock Profiles.

Figure 2 .
Figure 2. ANOVA repeated measures in CS-AMI phenotypes.Red-cardiac-only; blue-cardiorenal; and green-cardiometabolic; with median and interquartile ranges represented for cardiac power index (A), cardiac power index(RAP) (B), stroke volume (C), stroke volume index (D), systemic vascular resistance (E), and left ventricular stroke work index (F).The key is shown at the bottom.

Figure 2 .
Figure 2. ANOVA repeated measures in CS-AMI phenotypes.Red-cardiac-only; blue-cardiorenal; and green-cardiometabolic; with median and interquartile ranges represented for cardiac power index (A), cardiac power index (RAP) (B), stroke volume (C), stroke volume index (D), systemic vascular resistance (E), and left ventricular stroke work index (F).The key is shown at the bottom.

Table 1 .
Characteristics of patients with acute myocardial infarction cardiogenic shock phenotypes.